In the past several years, the demand for devices such as smartphones has grown exponentially. Multi-applications, such as calling, texting, gaming and internet browsing, make smartphones essential tools for people's everyday life. Technological advancements have facilitated the manufacturing of compact, inexpensive, and low-power consuming receivers as well as sensors for smartphones (devices), therefore, enabling the development of smartphone-based pedestrian navigation applications. Smartphones create limitless possibilities for navigation and positioning applications due to their sophisticated microprocessors, powerful operating systems, embedded sensors, and portable characteristics.
GPS (Global Positioning System), which is usually embedded in smartphones, provides an accurate position solution outdoors. However, the degradations and interruptions of GPS signals mean that GPS cannot be used to achieve accurate and continuous navigation solutions in challenging areas such as urban canyons, tunnels and indoors. On the other hand, the demand for navigation in these challenging areas is quickly increasing in various applications including: health care monitoring, logistics, Location Based Services (LBS), emergency services, tourism, and people management. The pedestrian navigation has been a popular research topic in the last decade.
As an alternative to GPS, self-contained navigation systems based on MEMS sensors can be applied to different applications, including mobile robot navigation and pedestrian navigation. Currently, there are two typical mechanizations for MEMS sensors to compute the navigation solution: INS (inertial navigation system) mechanization and PDR (pedestrian dead reckoning). The INS mechanization calculates the position, velocity, and attitude (PVA) of the object by integrating raw data from the accelerometers and gyroscopes. This mechanization can provide 3D PVA information, however, navigation errors by using this algorithm increase rapidly with time due to the drift characteristics of MEMS sensors.
To improve the MEMS navigation performance for pedestrians, PDR may reduce the accumulated speed of navigation errors. PDR has four critical procedures: step detection, step/stride length estimation, heading estimation, and 2D position calculation. The PDR provides a more accurate position solution than the INS mechanization, without other aiding sources, because it uses fewer integration calculations. The typical PDR algorithm usually assumes that the device is level (roll and pitch are zero degrees). Unfortunately, the roll and pitch cannot be ignored sometimes. In this case, PDR-based heading, calculated by the direct integration of the data from the vertical gyroscope, is inaccurate. The heading estimation error will finally affect the positioning accuracy. Furthermore, the PDR navigation solution still drifts with time. Therefore, both INS mechanization and PDR require additional aiding sources, such as GNSS, WLAN (Wireless Local Area Network, such as IEEE 802.11), and magnetometers, to reduce the navigation errors.
Many other approaches have been defined for pedestrian navigation, based on various types of hardware, including WLAN, ultra wideband (UWB), FM, and radio frequency identification (RFID), etc. However, pedestrian navigation in the challenging areas still unsolved due to several practical issues, such as special hardware designs and complicated infrastructure requirements. Most approaches, such as UWB and RFID, require special hardware or infrastructures to achieve accurate pedestrian navigation, which makes these approaches impractical. WLAN positioning does not require special hardware and is only based on WLAN infrastructures (routers), which have already have well-established in most public buildings such as universities, colleges, airports, shopping malls, and office buildings, making WLAN as the main aiding resource for MEMS sensors in challenging areas, such as indoors.
Fingerprinting and trilateration are two main approaches for WLAN positioning. Fingerprinting-based WLAN positioning usually has two operating phases: the pre-survey phase and the online positioning phase. In the pre-survey phase, Received Signal Strength (RSS) values from available access points (APs) and position information are collected as fingerprints for creating the radio map database. In the online positioning phase, the object's position is estimated by comparing observed RSS values with the fingerprints in the pre-built database. Trilateration-based WLAN positioning first calculates the ranges between the object and APs (routers) through the wireless signal propagation model. Then, the object's position is estimated by the use of trilateration. Fingerprinting usually provides more accurate position solutions at the cost of survey work in the pre-survey phase. These RSS based WLAN positioning methods usually have the following limitations: 1) they cannot provide complete navigation information (3D PVA); and 2) RSS values may have some blunders, which are affected by the environments, such as the multipath effect.
As such, there is a need for a method and apparatus for pedestrian applications to provide an enhanced navigation solution capable of accurately utilizing MEMS sensors' measurements from a device to determine the navigation state of the device/pedestrian while decreasing the effect of the above mentioned problems whether in the presence or in the absence of WLAN routers.